Sudden cardiac death (SCD) accounts for approximately 300,000 deaths in the United States per year and in most cases is the final result of ventricular arrhythmias that include ventricular tachycardia (VT) or ventricular fibrillation (VF). Ventricular arrhythmia is a severely abnormal heart rhythm (arrhythmia) that, unless treated immediately, is responsible for 75% to 85% of sudden deaths in persons with heart problems. Most ventricular arrhythmias are caused by coronary heart disease, hypertension, or cardiomyopathy, events that result in immediate death if not accurately diagnosed or treated. VT is a fast rhythm of more than three consecutive beats originating from the ventricles at rate of more than 100 beats per minute. VF is a rhythm characterized by chaotic activity of ventricles and causes immediate cessation of blood circulation and degenerates further into a pulseless or flat electrocardiogram record indicating no cardiac electrical activity.
An implantable cardioverter-defibrillator (ICD) has been considered the best protection against sudden death from ventricular arrhythmias in high risk individuals. However, most sudden deaths occur in individuals who do not have recognized high risk profiles. For long-term monitoring, electrocardiography is the criterion standard for the diagnosis of ventricular arrhythmia. If the clinical situation permits, a twelve lead electrocardiogram (ECG) is obtained and analyzed before conversion of the rhythm to detect any changes in the characteristics of the ECG signal. By extracting information about intervals, amplitude, and waveform morphologies of the different P-QRS-T waves, the onset of the ventricular arrhythmia can be detected. A wide range of algorithms and detection systems based on morphological, spectral, or mathematical parameters extracted from the ECG signal have been developed. Particular methods have shown that a combination of ECG parameters extracted from different algorithms may enhance the performance of the detection. Although these methods have exhibited advantages in the detection of ventricular arrhythmia, there are disadvantages as well. Some methods have proven quite difficult to implement or compute, while others demonstrate low specificity and low discrimination between normal and abnormal conditions. Moreover, most current methods involve a relatively late detection interval, which delays the initiation of life saving measures.
Machine learning techniques such as neural networks and support vector machines (SVM) have been suggested as useful tools to improve the detection efficiency. However, this strategy increases the overall requirements of the detection system if not utilized or employed properly. For example, selected ECG parameters should be relevant and show significant potential in the detection of ventricular arrhythmia. Otherwise, the efficiency of a machine learning task would decrease and degrade overall performance. Thus, what is needed are a high performance yet efficient medical device and method to enable early detection of the onset of ventricular arrhythmia.